CN105578480B - The pre- decision method of lack sampling frequency spectrum perception towards wide-band modulation converter - Google Patents
The pre- decision method of lack sampling frequency spectrum perception towards wide-band modulation converter Download PDFInfo
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W16/00—Network planning, e.g. coverage or traffic planning tools; Network deployment, e.g. resource partitioning or cells structures
- H04W16/14—Spectrum sharing arrangements between different networks
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04B—TRANSMISSION
- H04B17/00—Monitoring; Testing
- H04B17/30—Monitoring; Testing of propagation channels
- H04B17/382—Monitoring; Testing of propagation channels for resource allocation, admission control or handover
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L27/00—Modulated-carrier systems
- H04L27/0006—Assessment of spectral gaps suitable for allocating digitally modulated signals, e.g. for carrier allocation in cognitive radio
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
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- Y02D30/00—Reducing energy consumption in communication networks
- Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks
Abstract
The invention discloses a kind of pre- decision method of lack sampling frequency spectrum perception towards wide-band modulation converter mainly solves the problems, such as prior art false-alarm probability height and to calculate cost big.Its technical solution is: 1. receiving ends carry out compression sampling to the signal received, obtain the road m sample and calculate the time domain energy of each branch collecting sample；2. calculating test statistics r according to time domain energyJ, k；3. calculating the decision threshold γ of test statisticsJ, k；4 by test statistics compared with decision threshold, decision signal whether there is: if rJ, k>γJ, k, then it has been judged to signal, if all rJ, k≤γJ, k, then it is judged to no signal.The advantages of it is smaller that there is the present invention perceptual performance to be influenced by noise power, and computation complexity is low, effectively reduces influence of the non-sparsity of broadband Gaussian white noise to lack sampling frequency spectrum perception can be used in the broader frequency spectrum perception of analog signal compression sampling.
Description
Technical field
The invention belongs to fields of communication technology, are related to frequency spectrum perception technology, further relate to one kind towards wide-band modulation
The pre- decision method of lack sampling frequency spectrum perception of converter, the frequency spectrum perception that can be used in cognitive radio.
Background technique
Founder key technology of the frequency spectrum perception as cognitive radio system can provide necessary for Dynamic Closed Loop system
Feedback information provides environmental information and decision-making foundation for cognitive radio system/network, for orderly dynamically frequency spectrum distribution and conjunction
Reason ground spectrum regulatory provides safeguard, and is to improve the availability of frequency spectrum, improve wireless channel transmission conditions, intelligent network, actively hide
The basis kept away various interference, effectively carry out dynamic spectrum resource management.Current frequency spectrum sensing method is mostly absorbed in relatively narrow frequency
Find that frequency spectrum accesses chance within the scope of rate, by Shannon theorem it is found that available bandwidth directly determines theoretical maximum bit rate,
The access frequency spectrum that narrow band spectrum perception provides cannot obviously carry the traffic rate of user demand, and cognitive user is needed using wide
Band frequency spectrum perception finds out more access chances simultaneously in broader frequency range.Classical Nyquist sampling thheorem points out,
For undistorted reconstruct analog signal, the sample frequency of analog signal should at least more than being equal to twice of signal spectrum bandwidth,
When cognitive user carries out broader frequency spectrum perception, need to acquire sample from very wide frequency range with higher resolution ratio and extremely low power consumption
This, this give the digital signal processor system based on Shannon-nyquist sampling theorem bring acquisition front end conversion speed,
Height handle up large capacity cache space and back-end logic device operation highest frequency 'bottleneck' restrictions.
It is only necessary to the non-adaptive linear measurement sample points of only a few for compression sampling CS theory, so that it may pass through convex optimization
Method analog signal sparse in time domain or other transform domains is restored with great probability, can be directly real according to this method
To the conversion of information, this is provided existing signal from analog to reduce Analog-digital Converter rate and alleviating Digital Signal Processing pressure
New theoretical foundation.In consideration of it, compression sampling is combined with broader frequency spectrum perception, make full use of to the sparse of perceived spectral
Characteristic completes the information collection of broader frequency spectrum with the analog-to-digital conversion rate far below Nyquist sample rate, and with extremely low
Digital Signal Processing expense complete in real time broader frequency spectrum perception, be solve broadband analog signal acquisition and high-speed digital signal pass
One of defeated, storage and the effective way of processing bottleneck.
Currently, the broader frequency spectrum cognitive method that can be used for analog signal compression sampling is broadly divided into two classes:
The first kind is to be with the adduction of finite discrete multi-tone signal based on analog information converter AIC, AIC lack sampling framework
Then analog input signal model carries out integrating cumulative and low pass by the way that analog input signal is multiplied with random mark sequence
Sampling can use reconstructing method and obtain original signal or its statistical property, then pass through time domain after obtaining compression collecting sample
Or frequency domain energy detection method completes degree judgement, which realizes simply, but is restoring continuous spectrum letter in broadband
Number when, can face that input signal model mismatch generates great reconstructed error or the accurate bring higher-dimension of input signal model is multiple
The predicament of miscellaneous matrix operation.
Second class is that have translation invariant subspace based on wide-band modulation converter MWC, MWC lack sampling framework with limited
Union as analog input signal model, by the way that input signal is multiplied in multiple Lu Shangyu period random mark sequences,
The different weight factor down coversions of input signal Subspace Decomposition are realized, to reduce required sampling rate, by owing to adopt
The corresponding unlimited measurement vector of continuous analog signal acquisition is converted into multiple measurement vector system by all framework establishment, then
The corresponding support of occupied frequency range is found out using orthogonal matching pursuit algorithm, MWC lack sampling framework is reasonable with signal model, props up
The advantages that collection is determined in real time and can be realized with commercial device.However, since there are white noise, broadband white noises in the frequency range of broadband
It is not sparse on time domain, frequency domain or other transform domains, using the lack sampling sample of wide-band modulation converter, directly carry out wide
Band frequency spectrum perception, easily causes following problem:
(1) although any primary user's signal is not present in broadband frequency range to be perceived, white Gaussian noise is only existed, is pressed
The restructing algorithm of contracting perception can still provide the occupancy situation in frequency band according to sparsity, this will cause serious false-alarm probability；
(2) process determined using lack sampling sample progress signal reconstruction and support is brought and greatly calculates cost, and
These operations are meaningless；
(3) the frequency spectrum perception result of mistake influences the strategy of communication, brings secondary user's communication and frequently interrupts, reduces
The utilization rate of frequency band.
Summary of the invention
It is an object of the invention to overcome the above-mentioned deficiency of the frequency spectrum perception technology based on wide-band modulation converter, one is proposed
Lack sampling frequency spectrum perception pre- decision method of the kind towards wide-band modulation converter is non-sparse to effectively reduce broadband Gaussian white noise
Property influence to lack sampling frequency spectrum perception, promote frequency spectrum perception accuracy, reduce and calculate cost, improve band efficiency.
In order to complete above-mentioned purpose, the lack sampling frequency spectrum perception proposed by the present invention towards wide-band modulation converter is adjudicated in advance
Method includes the following steps:
(1) receiving end carries out compression sampling using wide-band modulation converter to the signal received, obtains the road m sample yi
(n), i=1,2 ..., m, n=0,1 ..., N-1, N are sample points, and calculate separately the time domain energy of each branch collecting sample
Amount
(2) test statistics is calculated according to time domain energy
Wherein,WithThe respectively time domain energy of jth branch and kth branch, j=2,3 ..., m, k=1,2 ...,
J-1 obtains m (m-1)/2 test statistics；
(3) test statistics r is calculatedj,kDecision threshold γj,k:
(3a) is by the time domain energy of i-th branchIt is transformed into frequency domain energyBy test statistics rj,kBy time domain
Frequency domain is transformed into,
(3b) constructs test statistics rj,kCumulative distribution function Pf:
Wherein ρj,kIt is the related coefficient of sum, value is Φ () function
Expression formula is
(3c) is according to the cumulative distribution function P of test statisticsf, using constant false alarm criterion, calculate decision threshold γj,k:
Wherein, PfFor the default false-alarm probability of each branch decision, Φ-1() is the inverse function of Φ () function；
(4) the test statistics r for obtaining step (2)j,kThe threshold value γ obtained with step (3)j,kCompare, determines whether
Signal, if it exists statistic rj,kGreater than thresholding γj,k, then it has been judged to signal；Any statistic r if it does not existj,kGreater than thresholding
γj,k, then it is judged to no signal.
The invention has the following advantages that
1, the present invention can effectively reduce the non-sparsity of broadband Gaussian white noise to deficient due to utilizing a small amount of compression sampling sample
The influence of sampling frequency perception；
2, the present invention due to its threshold value it is unrelated with noise variance, so perceptual performance influenced by noise power it is smaller,
And the influence to noise uncertainty can be effectively antagonized；
3, the present invention is due to only needing a small amount of sample to carry out simple computing module-square, therefore computation complexity is low, can meet frequency
Compose the real-time of perception.
Detailed description of the invention
Fig. 1 is implementation flow chart of the invention；
Fig. 2 is present invention correct detection probability analogous diagram under different signal-to-noise ratio；
Fig. 3 is present invention false-alarm probability analogous diagram under different signal-to-noise ratio；
Fig. 4 is the detection probability simulation comparison figure under different signal-to-noise ratio in the present invention when there are noise uncertainty；
Fig. 5 is present invention receiver performance curve analogous diagram under different false-alarm probabilities.
Specific embodiment
Broader frequency spectrum of the present invention for analog signal compression sampling perceives, and sensing terminal receives signal in each subchannel,
And the sampling for receiving signal is handled.
Referring to Fig.1, steps are as follows for realization of the invention:
Step 1, sensing terminal calculates the time domain energy T of each branch collecting samplei td。
Sensing terminal carries out compression sampling using wide-band modulation converter to the signal received, obtains the road m sample yi(n),i
=1,2 ..., m, n=0,1 ..., N-1；
Utilize branch sample yi(n) time domain energy is calculated
Wherein, m is the acquisition circuitry number of wide-band modulation converter, and N is sample points.
Step 2, test statistics r is calculated according to time domain energyj,k
Wherein,WithThe respectively time domain energy of jth and k branch, j=2,3 ..., m, k=1,2 ..., j-1,
M (m-1)/2 test statistics is calculated according to the different values of j and k.
Step 3, according to preset false-alarm probability, in the absence of primary user's signal, decision threshold γ is calculatedj,k。
In the absence of primary user's signal, receiving signal only includes noise, i.e. input signal x (t)=ω (t), it is assumed that ω (t)
For mean value be 0, variance isWhite Gaussian noise, decision threshold γj,kCalculating steps are as follows:
(3a) is by the time domain energy of i-th branchIt is transformed into frequency domain energyBy test statistics rj,kBy time domain
Frequency domain is transformed into,
(3b) constructs test statistics rj,kCumulative distribution function Pf:
(3b1) calculates Yi(k) statistical property:
(3b11) acquires structure according to wide-band modulation converter MWC compression, to branch sample yi(n) discrete fourier is carried out
Transformation, obtains branch sample spectra Yi(k):
Wherein, cilFor ± 1 sequence p of period pseudorandomi(t) cycle Fourier series expansion coefficient, L are that complete expression is defeated
Entering subspace number required for signal x (t) Fourier transformation X (j Ω), L can be calculated according to the following formula,
Wherein, fNYQIt is the equivalent Nyquist sampling rate of MWC, fpIt is the frequency of random mark sequence, fsIt is that low pass is adopted
Sample rate；
(3b12) in the case where primary user is not present, input signal spectrum X (k) be mean value be 0, variance isFrom
Stochastic variable is dissipated, using the mean value and variance of X (k), calculates branch sample spectra Yi(k) statistical property comprising:
Calculate Yi(k) mean value E [YiAnd variance D [Y (k)]i(k)] are as follows:
E[Yi(k)]=0,
Calculate Yi(k) mean value of real partAnd varianceAre as follows:
Calculate Yi(k) mean value of imaginary partAnd varianceAre as follows:
Calculate Yi(k) real part and Yi(k) related coefficient of imaginary part:(3b2) meter
Calculate branch sample spectra Yi(k) mould square mean value E [| | Yi(k)||2] and variance D [| | Yi(k)||2]:
Wherein,It is the variance of input signal spectrum X (k)；
(3b3) calculates branch sample spectra Yi(k) related coefficient of mould square:
The related coefficient of (3b31) on different frequent points are as follows:
cov[||Yp(a)||2,||Yq(b)||2]=0,
The related coefficient of (3b32) on identical frequency point are as follows:
Wherein p, q=1,2 ..., m, p ≠ q, k=0,1 ..., N-1,
C in formulaqmFor the cycle Fourier series expansion coefficient of ± 1 sequence of period pseudorandom,Expression takes real part,Expression takes imaginary part；
Mean value E [the T of (3b4) calculating branch frequency domain energyi fd], variance D [Ti fd] and correlation coefficient ρj,k:
(3b5) is according to the cumulative distribution function F of any two correlated Gaussian stochastic variable G and H ratioR(r), it calculates and examines
Statistic rj,kCumulative distribution function Pf:
FR(r) expression formula are as follows:
Wherein, μGFor the mean value of Gaussian random variable G,For the variance of Gaussian random variable G, μHFor Gaussian random variable
The mean value of H,For the variance of Gaussian random variable H, ρ is the related coefficient of G and H.
As N > 20, Ti fdApproximate Gaussian distributed, mean value and variance are respectivelyWithWith
Related coefficient be ρj,k, it can thus be concluded that test statistics rj,kCumulative distribution function be:
Wherein ρj,kIt is jth branch frequency domain energyWith kth branch frequency domain energyRelated coefficient, γ be examine system
Measure rj,kDecision threshold, E [] indicate mean value, D [] indicate variance；
(3c) is according to the cumulative distribution function P of test statisticsf, using constant false alarm criterion, calculate decision threshold γj,k:
Wherein, PfFor the default false-alarm probability of each branch decision, Φ-1() is the inverse function of Φ () function.
Step 4: the test statistics r that step (2) is obtainedj,kThe decision threshold value γ obtained with step (3)j,kCompared
Compared with determining whether signal:
Statistic r if it existsj,kGreater than thresholding γj,k, then it has been judged to signal；
Any statistic r if it does not existj,kGreater than thresholding γj,k, then it is judged to no signal.
Effect of the invention can be further illustrated by following emulation:
A, simulated conditions
The equivalent sampling rate for the wide-band modulation converter that analogue system uses is fNYQ=6GHz, acquisition circuitry number are m=
20, the period of ± 1 sequence of random mark is Tp=7.5ns, frequency fp=1/Tp, equivalent random chip number in a cycle
For L=45, low-pass filtering cutoff frequency used in each channel is fs/ 2, single channel sampling rate is fs=fp；Frequency range (0,
fNYQ/ 2) in, N=2 signal is co-existed in, the chip rate of each signal is sr=1.024MBaud, the carrier frequency of each signal
Rate is randomly generated, and the power of N number of signal is identical, and the ratio of the general power and noise power that define N number of signal is signal-to-noise ratio, every
1000 emulation is carried out under a signal-to-noise ratio, preset false-alarm probability is Pfa, compression acquisition, institute are carried out using MWC converter
With sample points K=100.
B, emulation content and result
Emulation 1: being -20dB~-10dB and default false-alarm probability P in signal-to-noise ratiofaUnder conditions of=0.01, to the present invention
Correct detection probability emulated, emulation is as shown in Figure 2.
From Figure 2 it can be seen that using K=100 lack sampling sample points, when signal-to-noise ratio is more than or equal to -8dB, it is of the invention just
True detection probability can achieve greater than 98%, and the present invention can obtain extremely superior pre- judgement in wider SNR ranges
Performance.
Emulation 2: being -20dB~-10dB and default false-alarm probability P in signal-to-noise ratiofaUnder conditions of=0.01, to the present invention
False-alarm probability emulated, emulation is as shown in Figure 3.
As seen from Figure 3, K=100 lack sampling sample points, in the range of signal-to-noise ratio -20dB~-10dB, this hair are utilized
Bright false-alarm probability inhibits substantially near default false-alarm probability value, and the threshold value that the present invention calculates is reasonable, can effectively overcome void
Alert generation.
Emulation 3: signal-to-noise ratio is -20dB~-10dB, false-alarm probability is 0.01 and the item of noise uncertainty ρ=1.4
Under part, correct detection probability of the invention is emulated, and it is carried out with the simulation curve that noise uncertainty is not present
Comparison, simulation result are as shown in Figure 4.
From fig. 4, it can be seen that there is no too big variations for correct detection probability of the invention, originally when there are noise uncertainty
The performance curve of invention illustrates that the present invention can have there are noise uncertainty and there is no essentially coinciding when noise uncertainty
Effect is to antinoise uncertainty.
Emulation 4: lack sampling frequency spectrum under conditions of signal-to-noise ratio is -16dB, to the present invention towards wide-band modulation converter
The receiver performance curve for perceiving pre- decision method is emulated, and by its with there are the emulation of noise uncertainty ρ=1.4 song
Line compares, and simulation result is as shown in Figure 5.
As seen from Figure 5, the pre- decision algorithm that the present invention provides can be in lesser false-alarm probability and lower signal-to-noise ratio item
Under part, more superior perceptual performance is obtained, when there are noise uncertainty, receiver performance curve of the invention is also basic
It is constant, illustrate that the present invention has robustness to noise uncertainty.
In summary analysis of simulation result, the present invention can obtain under the conditions of lesser false-alarm probability and lower signal-to-noise ratio
More superior perceptual performance is obtained, when there are noise uncertainty, perceptual performance is basically unchanged, and illustrates that the present invention can be effectively right
Antinoise uncertainty.
Claims (4)
1. a kind of pre- decision method of lack sampling frequency spectrum perception towards wide-band modulation converter, includes the following steps:
(1) receiving end carries out compression sampling using wide-band modulation converter to the signal received, obtains the road m sample yi(n), i=
1,2 ..., m, n=0,1 ..., N-1, N are sample points, and calculate separately the time domain energy T of each branch collecting samplei td:
(2) test statistics is calculated according to time domain energy
Wherein,WithRespectively the time domain energy of jth branch and kth branch, j=2,3 ..., m, k=1,2 ..., j-1 are obtained
To m (m-1)/2 test statistics；
(3) test statistics r is calculatedj,kDecision threshold γj,k:
(3a) is by the time domain energy T of i-th branchi tdIt is transformed into frequency domain energy Ti fd, by test statistics rj,kIt is transformed by time domain
Frequency domain,
(3b) constructs test statistics rj,kCumulative distribution function Pf:
Wherein ρj,kIt isWithRelated coefficient, value isD [] indicates variance,
Φ () function expression is
(3c) is according to the cumulative distribution function P of test statisticsf, using constant false alarm criterion, calculate decision threshold γj,k:
Wherein, Φ-1() is the inverse function of Φ () function；
(4) the test statistics r for obtaining step (2)j,kThe threshold value γ obtained with step (3)j,kIt is compared, determines whether
Signal, if it exists statistic rj,kGreater than thresholding γj,k, then it has been judged to signal；Any statistic r if it does not existj,kGreater than thresholding
γj,k, then it is judged to no signal.
2. the lack sampling frequency spectrum perception pre- decision method according to claim 1 towards wide-band modulation converter, wherein walking
Suddenly each branch collecting sample y is calculated in (1)i(n) time domain energy Ti td, it calculates according to the following formula:
3. the lack sampling frequency spectrum perception pre- decision method according to claim 1 towards wide-band modulation converter, wherein walking
Suddenly by the time domain energy T of i-th branch in (3a)i tdIt is transformed into frequency domain energy Ti fd, it is to utilize pa Savall law of conservation of energy,
It calculates according to the following formula:
Wherein, YiIt (k) is yi(n) discrete Fourier transform, i=1,2 ..., m, n=0,1 ..., N-1.
4. the lack sampling frequency spectrum perception pre- decision method according to claim 1 or 3 towards wide-band modulation converter, wherein
Test statistics r is constructed in step (3b)j,kCumulative distribution function Pf, it carries out as follows:
(3b1) calculates Yi(k) statistical property:
(3b11) acquires structure according to wide-band modulation converter MWC compression, to branch sample yi(n) discrete Fourier transform is carried out,
Obtain branch sample spectra Yi(k):
Wherein, cilFor ± 1 sequence p of period pseudorandomi(t) cycle Fourier series expansion coefficient, L are complete expression input letters
Subspace number required for number x (t) Fourier transformation X (j Ω), L can be calculated according to the following formula,
Wherein, fNYQIt is the equivalent Nyquist sampling rate of MWC, fpIt is the frequency of random mark sequence, fsIt is low pass sampling speed
Rate；
(3b12) in the case where primary user is not present, input signal spectrum X (k) be mean value be 0, variance isDiscrete Stochastic
Variable calculates branch sample spectra Y using the mean value and variance of X (k)i(k) statistical property:
Calculate Yi(k) mean value E [YiAnd variance D [Y (k)]i(k)] are as follows:
E[Yi(k)]=0,
Calculate Yi(k) mean value of real partAnd varianceAre as follows:
Calculate Yi(k) mean value of imaginary partAnd varianceAre as follows:
Calculate Yi(k) real part and Yi(k) related coefficient of imaginary part:
(3b2) calculates branch sample spectra Yi(k) mould square mean value E [| | Yi(k)||2] and variance D [| | Yi(k)||2]:
Wherein,It is the variance of input signal spectrum X (k)；
(3b3) calculates branch sample spectra Yi(k) related coefficient of mould square:
The related coefficient of (3b31) on different frequent points are as follows:
cov[||Yp(a)||2,||Yq(b)||2]=0,
The related coefficient of (3b32) on identical frequency point are as follows:
Wherein p, q=1,2 ..., m, p ≠ q, k=0,1 ..., N-1,
cpm、cqmFor the cycle Fourier series expansion coefficient of ± 1 sequence of period pseudorandom,Expression takes real part,Table
Show and takes imaginary part；
Mean value E [the T of (3b4) calculating branch frequency domain energyi fd], variance D [Ti fd] and correlation coefficient ρj,k:
(3b5) is according to the cumulative distribution function F of any two correlated Gaussian stochastic variable G and H ratioR(r), inspection statistics are calculated
Measure rj,kCumulative distribution function Pf:
FR(r) expression formula are as follows:
Wherein, μGFor the mean value of Gaussian random variable G,For the variance of Gaussian random variable G, μHFor Gaussian random variable H's
Mean value,For the variance of Gaussian random variable H, ρ is the related coefficient of G and H；
As N > 20, Ti fdApproximate Gaussian distributed, mean value and variance are respectivelyWith WithPhase
Relationship number is ρj,k, it can thus be concluded that test statistics rj,kCumulative distribution function be:
Wherein ρj,kIt is jth branch frequency domain energyWith kth branch frequency domain energyRelated coefficient, γ is test statistics
rj,kDecision threshold, E [] indicate mean value, D [] indicate variance.
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| CN107181548A (en) * | 2017-05-15 | 2017-09-19 | 西安电子科技大学 | One kind compression frequency spectrum perception performance improvement method |
| CN107332566B (en) * | 2017-06-19 | 2020-09-08 | 四川大学 | MWC-based support set rapid recovery method |
| CN107426737B (en) * | 2017-09-22 | 2020-10-02 | 哈尔滨工业大学 | Broadband spectrum sensing method based on single-channel structure modulation broadband converter |
| CN109495198A (en) * | 2019-01-25 | 2019-03-19 | 西安电子科技大学 | Lack sampling frequency spectrum sensing method based on the detection of residual error correlation matrix |
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